In surveillance for facial recognition, 3D information can be used to reliably identify a human face from facial feature data. In addition, 3D depth information can help in monitoring and image understanding.
One known method of capturing 3D depth information is using depth cameras—image features are matched between a pair of views taken a distance apart (“baseline”). Conventionally, a baseline defines a region of reliable 3D space where depth information is reliable. In order to “triangulate” a 3D point, two views of the 3D point are identified, and the 3D point is calculated by back-projecting and intersecting two rays (3D lines) from each camera. However, if the two 3D lines are too close to be almost parallel, the intersection point is poorly defined.
Therefore, in order to obtain the reliable depth information close to the cameras, the baseline between the cameras needs to be small to ensure that: (a) both cameras can see the 3D point, and (b) the angle between the rays is large enough. Similarly, in order to obtain the reliable depth far from the camera, the stereo baseline needs to be much larger.
Hence, how to accurately obtain depth information is an important issue in this field.
It should be noted that the above description of the technical background is merely for the purpose of facilitating a clear and complete description of technical solutions of the present invention, and is convenient for understanding by those skilled in the art. The above technical solutions should not be considered to be well-known to those skilled in the art, simply because these aspects are set forth in background section of the present invention.